Analyzing the Effectiveness of Phishing at Network Level Sagar - - PowerPoint PPT Presentation

Analyzing the Effectiveness of Phishing at Network Level

Sagar Mehta, Nitya Sundareswaran, Kevin D. Fairbanks, Nick Feamster

Motivation

• Source - Phishing Activity Trends Report July, 2006 , Anti-Phishing workgroup
• Our work done from Jan 07 – Apr 07

Related Work

Mostly at application layer

• Why phishing works ? – Dhamija et al
• The Battle Against Phishing:Dynamic Security Skins - Dhamija et al
• Detection of Phishing pages based on visual similarity - Liu et al
• Phoney: Mimicking User Response to Detect Phishing Attacks -Chandrasekaran et al
• A Framework for Detection and Measurement of Phishing Attacks - Doshi et al
• Anti-Spam Techniques

Problem Statement

• Looking at the effectiveness of Phishing from network

level = Complementary approach to application layer analysis

• Correlate Phishing mails to outgoing traffic
• Analyze traffic destined to Phishing sites

System Architecture

Data sources

• Spam Trap data
• Netflow Records
• DNS cache

Parsing script

• Parsing script to obtain urls from spam
• Filter using heuristics to obtain phishing urls
• anchor text and actual link disagree
• redirection – http 302, meta keyword
• presence of certain keywords
• presence of ip address in place of domain name
• Caveats:
• Human intervention for correct interpretation of URL
• http://www.example-com, Replace “-”with “.” In the above link
• http://www.example .com, Remove space in the above link
• Attached .jpg images that provide the URL address – no OCR
• Deceptive user names e.g. ‘www.example1.com@example2.com’

Querying Script

Querying script to map phishing domains to IP addresses Simulating HTTP client to follow redirects

• Status code 300-307 in HTTP response
• Meta redirects

Caveat

• Avoid corrupting the trace while mapping phishing domains to IP addresses

by directing queries to a foreign name server Extracted ip addresses to further query netflow data from GTRNOC to get netflow tuples using src ip, src port , dest ip, dest port as ‘key’

Interaction with known phishing Sites from PhishTank – wide varation in byte distribution even when interacting with sites imitating the same website

Similar variation in connection time distribution even when interacting with sites imitating the same website

How many unique phishing sites did a source address visit ?

How many times a connection was made to a phishing site ?

96 hour window around the receipt of Bank of America phishing email in the spam trap

Connections made by diff src addresses to Bank of America phishing site – Observations in line with “persistent connection behavior of browsers” by wang et al

Bytes Percentage

Seconds Percentage

Challenges while analyzing phishing at network level

• Lack of application layer context
• Not everybody sees the same set of spam/phishing emails
• Redirection Techniques
• Avg lifetime of a phishing site typically very small
• Timing differences
• Multiple Domain Hosting
• Other researchers on the same network

Recommendations and Future Work

• Combined Data Sources
• Application Level Sources
• DNS Traces
• Multiple Vantage Points - Different Universities with

Spam Traps

• Can help address questions about -
• Targeted Phishing
• Percentage Phishing Mails per Spam Trap

Acknowledgements

• "The logs and netflow traces used in this work were

made available by the Georgia Tech Research Network Operations Center (www.rnoc.gatech.edu)

96 hour window around the receipt of phishing email about site hosted on yahoo geocities in the spam trap

Bytes Percentage

Seconds Percentage